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Clarify Research Idea

Community
cpt13-g
clarify-research-idea

Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.

Overview

Publishercpt13-g
Repositoryclarify-research-idea
Skill nameclarify-research-idea
Stars
264
Forks
6
Bundled files
8
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cpt13-g on GitHub. Read the source before you install it.

Installation

Install the Clarify Research Idea AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cpt13-g/clarify-research-idea/tree/main/.agents/skills/clarify-research-idea
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cpt13-g/clarify-research-idea.git /tmp/clarify-research-idea
mkdir -p .claude/skills
cp -r /tmp/clarify-research-idea/.agents/skills/clarify-research-idea .claude/skills/clarify-research-idea
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clarify Research Idea in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Clarify Research Idea on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Clarify Research Idea is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Clarify Research Idea

Convert an early research direction into an auditable idea architecture before detailed experiment design or manuscript writing. Preserve the researcher's judgment: make assumptions and framing visible, challenge weak novelty, and never manufacture supporting evidence.

Choose the operating mode

Use guided by default when the question, gap, or method is uncertain. Ask one high-information question at a time, maintain a visible working draft, and pause at the three checkpoints.

Use synthesize when the user supplies enough material or explicitly requests a one-pass result. Complete the brief with labeled assumptions; mark checkpoint-dependent claims UNCONFIRMED and request confirmation at the end.

State the selected mode, research domain, maturity level, and intended deliverable in one short line. Let the user correct them cheaply.

Run the heuristic interaction loop

Read interactive-heuristics.md in guided mode, whenever the user's idea is vague, or whenever the user asks for interactive clarification. Treat interaction as part of the method, not as casual clarification.

Use this turn pattern:

  1. State the current bottleneck in one sentence.
  2. Ask one high-information question that will change the idea architecture.
  3. Offer 2-3 plausible answer directions when the user is unsure.
  4. Update the visible Working Idea Canvas with provenance labels before asking the next question.

Do not overwhelm the user with a survey. If multiple questions are useful, choose the one that best constrains the task, gap, hypothesis, or evidence path. If the user asks for a fast one-pass result, switch to synthesize and mark unconfirmed decisions explicitly.

Maintain provenance

Label substantive content throughout the workflow:

  • PROVIDED: stated or supplied by the user.
  • VERIFIED: supported by a checked primary source or supplied artifact.
  • INFERRED: reasoned from available material but not directly established.
  • PROVISIONAL: useful working hypothesis awaiting evidence or user confirmation.
  • UNKNOWN: missing and consequential.

Do not silently upgrade one status to another. When browsing or academic-search tools are available, verify time-sensitive literature claims and bibliographic metadata. Treat failure to find a paper as search incompleteness, not proof of novelty.

Run the workflow

0. Scope the session

Determine the application, research object, current idea, intended venue or audience, available data or system, resource limits, and desired output language. Stay centered on AI, computer science, data-driven science, and engineering-method papers. If the request is primarily qualitative or non-methodological, explain that the mathematical contract must be adapted before continuing.

Identify whether the user has: a broad interest, a question without a task definition, a method without positioning, anchor papers without a gap, or a nearly complete idea needing audit. Use the maturity triage in interactive-heuristics.md to choose the first question. Reuse supplied documents and prior decisions instead of re-asking settled questions.

1. Establish the Task Contract

Read task-formalization.md. Define the inference-time inputs, required outputs, data or interaction process, model or decision function, objective, constraints, evaluation measures, baselines, assumptions, and explicit exclusions. Use mathematical notation only when it removes ambiguity; do not invent a loss function for appearance.

Require alignment among the claimed problem, information available at use time, optimization target, and evaluation metric.

Checkpoint 1 — Task Contract: Present the one-sentence task, compact formulation, success criterion, assumptions, and scope boundaries. In guided mode, obtain user confirmation before treating the contract as fixed.

2. Make the argument and framing explicit

Read framing-and-motivation.md. Build the chain:

background -> consequential friction -> unresolved cause -> required capability -> proposed research move

Record the intended reader update, the current default view, and the strongest counter-position in a Framing Intent block. Allow framing to guide emphasis and argument order, but never let it bias evidence retrieval, omit material counterevidence, or turn a hypothesis into a fact.

Express each challenge as a causal requirement, not an adjective: property of the setting -> technical difficulty -> why generic methods fail -> capability the solution must provide.

3. Position against 3-5 anchor papers

Read anchor-paper-positioning.md. Select papers by role rather than fame: problem anchor, method anchor, closest competitor, experiment anchor, and optional writing/architecture anchor. A paper may fill more than one role.

Verify title, authors, year, venue or repository, and DOI or stable URL. Extract WHY / HOW / WHAT / LIMIT / TRANSFER for each paper. Compare task assumptions and evidence, not only module names. Derive high-level writing characteristics without copying distinctive wording.

4. Derive and attack the idea

Read novelty-and-falsification.md. Consider three routes: a genuinely new problem, an old problem with an ignored condition or failure mode, or new evidence/understanding that changes a boundary. Force each candidate through:

observed gap -> underlying cause -> design principle -> method component -> expected evidence

Generate alternatives only when they help the user choose. Reduce the selected idea to one core hypothesis and 2-3 contribution cards. Attack it with the closest existing method, the simplest plausible baseline, alternative explanations, confounds, negative cases, and feasibility limits. In guided mode, run the reviewer-simulation questions from interactive-heuristics.md before treating a contribution as stable.

Checkpoint 2 — Positioning and Hypothesis: Present the verified anchors, defensible gap, rejected alternatives, core hypothesis, and falsifier. In guided mode, obtain user confirmation before freezing the method architecture.

5. Design the method architecture and core figure

Read framework-figure.md. Map every challenge to a design principle, every principle to a component or procedure, and every component to a claim and validation hook. Build an editable textual specification and a Mermaid draft using:

context/input -> problem structure -> method components -> objective/constraints -> output -> evidence

Keep the figure causal and selective. Do not add decorative modules merely to make the method look complex. If the user requests an actual .pptx, hand the approved figure specification to a presentation-generation workflow rather than making PowerPoint a hidden dependency of this skill.

6. Compile and audit the Research Idea Brief

Read output-schema.md. Produce one coherent brief, not disconnected brainstorming notes. Include the Task Contract, Framing Intent, anchor-paper matrix, gap, hypothesis, contribution cards, challenge-solution map, method architecture, claim-evidence matrix, Mermaid figure, falsification plan, risks, and unresolved decisions.

Score readiness on clarity, positioning, novelty, feasibility, falsifiability, and claim-method-evidence consistency. Treat the score as a diagnostic, not scientific proof. Include the final Working Idea Canvas and checkpoint decisions so later experiment-design and paper-writing workflows can inherit the reasoning path. Do not declare the idea ready while a critical item is UNKNOWN.

Checkpoint 3 — Final Idea Architecture: Ask the user to approve or revise the final hypothesis, contributions, method figure, and unresolved high-risk assumptions. Mark the brief CONFIRMED, PROVISIONAL, or BLOCKED accordingly.

Enforce integrity rules

  • Never fabricate papers, identifiers, results, datasets, mechanisms, novelty, or feasibility.
  • Never present desired rhetorical direction as empirical evidence.
  • Never claim novelty solely because the current search did not find prior work.
  • Never define a contribution with prestige adjectives such as “novel,” “robust,” or “comprehensive” without a concrete difference and validation path.
  • Bind every contribution to an observable claim and at least one fair comparison, ablation, diagnostic, stress test, or boundary test.
  • Preserve inconvenient counterevidence and explain whether it weakens, redirects, or bounds the idea.
  • Prefer a simpler idea when it explains the same challenge with fewer assumptions.

Handoff boundary

Stop after the confirmed Research Idea Brief and core-figure specification. Provide experiment hooks, not a fully executed experiment campaign. Route exhaustive literature reviews, detailed experiment implementation, full manuscript drafting, language polishing, and final PPT production to their corresponding workflows while carrying forward the confirmed Task Contract, claim-evidence matrix, anchor papers, and decision status.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Clarify Research Idea AI skill do?

Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper fig...

Why use Clarify Research Idea on TypingMind?

Because you install it once and use it with any model. Clarify Research Idea is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Clarify Research Idea in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cpt13-g/clarify-research-idea/tree/main/.agents/skills/clarify-research-idea. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Clarify Research Idea?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Clarify Research Idea?

As many as you like. As long as a model supports skills, you can use Clarify Research Idea with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Clarify Research Idea AI skill free?

It is published on GitHub by cpt13-g. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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